onlineappendix … · our baseline estimates only include household income in the second (or third...
TRANSCRIPT
Online Appendix
The Spending and Debt Response to Minimum Wage Hikes
By Daniel Aaronson, Sumit Agarwal, and Eric French∗
∗ Authors’ affiliations are Federal Reserve Bank of Chicago. Contact information: Daniel Aaronson at [email protected], Sumit Agarwal at [email protected], or Eric French at [email protected]. The views expressed inthis paper do not necessarily reflect those of the Federal Reserve Bank of Chicago or the Federal Reserve System.
1
2 THE AMERICAN ECONOMIC REVIEW MONTH YEAR
Appendix A: Data Appendix
The Consumer Expenditure Survey (CEX)
The empirical analysis primarily relies on the 1983 to 2008 CEX and is briefly described in
section 2.1 In this appendix, we provide further details about the sample selection criteria.
Our sample is driven by requirements to compute S. This is particularly relevant in two
cases. State codes are needed to know effective minimum wage levels, but the CEX does not
report actual state of residence for the 10 percent of the sample residing in smaller states.
These observations are dropped.2 Another 16.7 percent of the remaining sample are excluded
because of incomplete income responses.
To further refine the sample to households with adults that have well-measured hourly
wages, we also exclude the self-employed (8.7 percent of remaining sample)3, households
headed by those under 18 or over 64 (21 percent), households in the survey for only one period
(11.5 percent), households without an initial wage for the head and spouse (14.7 percent),
and households where either of the two member’s hourly wage is only 60 percent (that is,
implausibly low) or 40 times greater than the effective minimum wage in the initial survey (4.2
percent). Finally, we exclude 5.5 percent of the remaining sample because of large changes
in family composition (either the number of kids or the number of adults changes by more
than 2), head’s age (greater than two years), or head’s gender, or log hourly wages between
the initial survey and the last survey (log change of 1.5 of greater). These restrictions are
meant to reduce the impact of measurement error or to exclude large and difficult-to-model
changes in circumstances likely unrelated to minimum wage legislation.
1We do not include the 1981-82 panels because of some concern raised in Attansio and Weber (1995) about dataquality and because of nontrivial differences in data structure and design. That said, there were no state increasesbetween 1980 and 1982. The federal increases in 1980 and 1981 would be absorbed by time dummies because no statewas above the prior federal minimum wage either.
2The CEX assigns states to these residents. Our results do not change if we use the CEX-assigned state rather thandropping those residents. We also drop the District of Columbia because of its complicated minimum wage structure.
3The percentages reported are ordered in that each one reflects the share of excluded observations relative to thesample that remains up to that point.
VOL. VOLUME NO. ISSUEONLINE APPENDIXTHE SPENDING AND DEBT RESPONSE TO MINIMUM WAGE HIKES3
For spending, we ultimately use 200,549 household-surveys, representing 60,838 households.
Of these, 11.2 percent, or 22,474 household-surveys, are from households with some minimum
wage income in the initial period (i.e. Si > 0). Just under 16,000 are from families where
minimum wage income makes up over 20 percent of total pre-tax income (i.e. Si ≥ 0.2). For
income, we use the same 60,838 households but because income is essentially only asked in
the first and last surveys, we use 104,788 household-surveys.4
Panel A of web table A1 includes descriptive statistics of the key variables, including real
total, durables, and nondurables and services spending, real family income, and selected
demographics.
The Survey of Income and Program Participation (SIPP) and The Current
Population Survey (CPS)
To provide corroboration of the income results estimated from the CEX, we also compute
the income response to a minimum wage hike using the SIPP and CPS. The main advantage
to these datasets is that they provide larger samples and are specifically designed to collect
high-quality earnings and wage information.
The first SIPP panel we use begins in 1986 and the last ends in 2007. Each panel lasts
between two and four years and provides interviews with between 12 and 40 thousand house-
holds. Households are interviewed every four months during the time they remain in a panel.
While they are asked to recall labor market information for each month between interviews,
we only use the most recent month information.
Variables are coded, and wage, self-employment, and family composition restrictions are
introduced, to be as close as possible to the CEX sample described above. Like the CEX,
the numerator on Si – total income from minimum wage earners – is also computed on the
4Whenever an individual is added or deleted from the consumer unit, their income is collected regardless of thesurvey. Our baseline estimates only include household income in the second (or third if income is missing in the second)and fifth survey. However, we have also included household-surveys where a new worker was added. The results are verysimilar to those reported in table 1. The weighted average income response (table 1, column 4) is virtually identical.
4 THE AMERICAN ECONOMIC REVIEW MONTH YEAR
household head and, when applicable, spouse or nonmarried partner, only in the first period
that we observe them.
The one important difference, relative to the CEX, is that we restrict the SIPP sample
to households with an hourly worker. This restriction is meant to increase the liklihood
that minimum wage workers are correctly identified. As can be seen in table A1, this also
reduces the family income of the Si = 0 control group.5 There are 474,758 household-survey
observations remaining after all our sample restrictions,6 of which 11.4 percent report some
minimum wage earnings and 8.3 percent report at least 20 percent of their total household
nonproperty income from minimum wage earners.
Panel B of table A1 provides summary statistics for the key SIPP variables.
The CPS data that we use begins in 1980 and ends in 2007. Individuals are in the CPS for
four months, out for the following eight, and then in again for four more months. Those in
the fourth and eight months of their participation are known as the outgoing rotation files
and are asked questions specifically about weekly earnings and hours and hourly wages for
those paid-by-the-hour. Therefore, we have two responses for each CPS respondent. Again,
we define variables and sample restrictions to be analogous to the CEX.
Like the SIPP, variables are coded, and wage, self-employment, and family composition
restrictions are introduced, to be as close as possible to the CEX sample. The numerator on
Si is likewise computed on the household head and, when applicable, adult second earner, in
the first period that we observe them.
Using the sample of hourly wage workers, there are 809,631 observations remaining after
our sample restrictions, of which 15.0 percent report some minimum wage earnings and 11.6
5We can compute a wage from monthly income and monthly hours worked, which is more analogous to the CEXwage measure. In this case, SIPP mean income would be about 20 percent higher.
6The definition of a household is not as straightforward as in the CEX. We rely on the variable ppentry to definehouseholds. Experimentation with other methods, such as holding composition fixed (stable households), does notqualitatively change the results.
VOL. VOLUME NO. ISSUEONLINE APPENDIXTHE SPENDING AND DEBT RESPONSE TO MINIMUM WAGE HIKES5
percent report at least 20 percent of their total household nonproperty income from minimum
wage earners. Panel C of web table A1 provides summary statistics for the key variables.7
Credit Bureau Reports
We use a proprietary dataset from a large financial institution that issues credit cards
nationally. See Agarwal, Liu, and Souleles (2007) for details. We primarily rely on the
credit bureau reports that are appended to these accounts because it allow us to look at
the portfolio of debt of these households and test whether the financing of large durables,
particularly vehicles, rise after a minimum wage increase.
There are important limitations to this data that give us some pause. First, by construction,
the sample is selected on individuals holding a credit card. Minimum wage workers with
credit cards are plausibly a selected sample of all minimum wage workers. According to our
estimates from the Survey of Consumer Finances, 45 percent of all minimum wage workers
have a credit card. This is similar to Johnson’s (2007) estimate that 43 percent of households
in the bottom quintile of the income distribution own a credit card. Median quarterly income
is $3,656 and $3,047, median durables are $9,463 and $2,291, and median voluntary equity is
$3,663 and $452 for those with and without a credit card, respectively. Thus it appears that
we are selecting on a group of minimum wage workers who are less borrowing constrained
than others. Second, as section II notes, demographics and income measures are limited. In
particular, we only have the annual income of the account holder at the time of application.
However, that data allows us to compute the probability that a worker is paid at the minimum
wage (see section II.A).
Panel D of web table A1 provides some key descriptive statistics.
The Survey of Consumer Finances (SCF)
7Mean family income is significantly higher, about $51,000 for Si = 0 households, if the sample is not restricted tohourly workers.
6 THE AMERICAN ECONOMIC REVIEW MONTH YEAR
Finally, we use the SCF to provide descriptive information on the initial joint distribution
of the state variables used in the dynamic programming problem. The three state variables
are the permanent component of income Pit, cash on hand Xit (which is the sum on income
and net financial assets), and the stock of durable goods Dit. Equation (??) shows that
Pit = Yit − αt when there are no transitory shocks, so we just need Yit to infer Pit. We
assume that permanent income is the same as current income, and define the durables stock
as the sum of vehicles plus the stock of non-vehicle durables. We define net financial assets
as financial assets less debt against these financial assets or durable goods.
Web table A4 presents descriptive statistics from the 1989, 1992, 1995, 1998, 2001, 2004,
and 2007 waves of the SCF. The table includes the state variables as well as total debt and
assets which contain other assets, such as housing and business wealth, to provide a more
complete picture of household balance sheets.
We present means for both minimum wage households (Si = 0) and above minimum wage
households (S ≥ .2). To compute Si, we use a methodology very similar to the CEX (de-
scribed in section 3.1). First, we define someone as a minimum wage worker if that individual
makes between 60 and 120 percent of the minimum wage. Next, if an individual is a minimum
wage worker, we multiply that individual’s hourly wage by hours per week times weeks per
year. Because the SCF reports pay at frequencies chosen by the respondent, we compute
the wage using given pay and frequency of pay, adjusted appropriately by hours per year.
Finally, we take total household income from minimum wage workers and divide through by
total household wage income (where wage income is the income of respondent and spouse
and is derived using the procedure described above) which gives Si, the share of income from
minimum wage workers.
Web table A4 shows that for minimum wage households8, mean income, durables, and
8Similar to the CEX, the unit of observation in the SCF is the “primary economic unit,” which is usually a household.
VOL. VOLUME NO. ISSUEONLINE APPENDIXTHE SPENDING AND DEBT RESPONSE TO MINIMUM WAGE HIKES7
durables debt are all about one half to one third as large as for non-minimum wage households.
However, mean net financial wealth of minimum wage households is only 16 percent of that
of non-minimum wage households. Median net financial assets are only $180. Note that
that our definition of assets and durables excludes housing and business wealth. Roughly
40 percent of all minimum wage households own their home. For these households, housing
represents close to 50 percent of all wealth and housing debt represents over 50 percent of all
debt.
State-level Data
We obtained the state minimum wage histories from the January issues of the Monthly
Labor Review. See web table A2.
When estimating the effect of the minimum wage on spending and income, we some-
times control for maximum cash welfare benefit for a family of three by state and year,
the refundable EITC attainable in a state in a given year, and state unemployment rates
to account for possible UI extensions. The welfare levels are obtained from past issues
of the Greenbook. For the years 1981, 1988, 1996, and 2006, we used table 7-22 from
the 2008 Greenbook (http://waysandmeans.house.gov/media/pdf/110/tanf.pdf). For the
years 1994, 1998, 2000, 2002, and 2003, we used table 7-10 from the 2003 Greenbook
(http://waysandmeans.house.gov/media/pdf/greenbook2003/Section7.pdf). We were unable
to find 1997, 1999, 2001, 2004, 2005, and 2007 and therefore assumed that they were the same
as the following year (in most cases the previous and following year were the same). All re-
maining years were obtained from Diane Schanzenbach and are based on past Greenbooks.
The annual EITC measure is the refundable EITC attainable in a state as a percent of the
attainable federal EITC. We take this from Baughman and Dickert-Conlin (2007) through
1999 and table I-2 in http://www.cga.ct.gov/2008/rpt/pdf/2008-R-0102.pdf thereafter. In
In order to preserve confidentiality of respondents, noise is added to SCF data. Each responding economic unit is turnedinto five observations.
8 THE AMERICAN ECONOMIC REVIEW MONTH YEAR
some instances (e.g. Iowa), the sources conflict, in which case we use the Baughman and
Dickert-Conlin number. State unemployment rates are taken from the BLS’ tabulation of the
Current Population Survey. Note that the correlation between the change in the state mini-
mum wage and the change in state EITC and welfare benefits are essentially zero, consistent
with out finding that these additional controls have little impact on our minimum wage point
estimates.
Appendix B: Standard error calculation when averaging over multiple estimates(not for
publication)
Define the population marginal propensity to spend (MPS) as β and the estimated MPS
as β = CY, where C = the estimated coefficient on the minimum wage from a regression
of total spending (so C includes durables investment) on the minimum wage (which at the
population level we define as C), Y = the estimated coefficient on the minimum wage from
a regression of income on the minimum wage (which at the population level we define as
Y ). The spending estimate comes from the CEX, which we define as C = C + εC . We
have three estimates of the income response from the CEX, SIPP, and CPS, defined as
ˆYCEX ≡ Y + εYCEX, ˆYSIPP ≡ Y + εYSIPP
, ˆYCPS ≡ Y + εYCPS. We assume that εC and εY.
are white noise. We take the weighted average of these estimates for our estimated income
response,
Y = wCEXˆYCEX + wSIPP
ˆYSIPP + (1− wCEX + wSIPP ) ˆYCPS ≡ Y + εY .(1)
A Taylor’s series expansion for β is
β = β +1
YεC −
C
Y 2εY
VOL. VOLUME NO. ISSUEONLINE APPENDIXTHE SPENDING AND DEBT RESPONSE TO MINIMUM WAGE HIKES9
so the variance is:
V ar(β) = E(β − β)2 =1
Y 2V ar(εC) +
C2
Y 4V ar(εY )− 2
C
Y 3Cov(εC , εY ).(2)
Our estimate of V ar(εC) is the variance of the estimated coefficient C (or the square of its
standard error). Next, we estimate V ar(εY ) using equation (1)
V ar(ǫY ) = V ar(Y ) = w2CEXV ar( ˆYCEX) + w2
SIPPV ar( ˆYSIPP ) + (1− wCEX + wSIPP )2V ar( ˆYCPS)
(3)
where V ar( ˆYCEX), ... are the variance of the coefficients ˆYCEX , ... Finally, consider estimating
Cov(εC , εY ). This will be nonzero because the CEX is used to estimate both C and Y .
Analogous to equation (2) we can recover this covariance using:
V ar( ˆβCEX) =1
ˆYCEX2V ar(εCCEX
) +ˆCCEX
2
ˆYCEX4 V ar(εYCEX
)− 2ˆCCEX
ˆYCEX3Cov(εCCEX
, εYCEX)
where ˆβCEX is the 2SLS estimate of β using the CEX. Rearranging yields
Cov(εCCEX, εYCEX
) =ˆY 3
CEX
2 ˆCCEX
[
1
ˆYCEX2V ar(εCCEX
) +ˆCCEX
2
ˆYCEX4 V ar(εYCEX
)− V ar( ˆβCEX)
]
.
(4)
Because the SIPP and CPS estimates come from different data sets, the covariance of the
income estimates with either the income or spending estimates in the CEX should be 0. Thus
Cov(εC , εY ) = Cov(εC , Y − Y )
= Cov(εC , wCEXεYCEX)
= wCEXCov(εC , εYCEX).(5)
Thus V ar(β) can be estimated using equation (2), using equations (4) and (5) to estimate
Cov(εC , εY ), and (1) to estimate V ar(Y ).
Including Debt Information
10 THE AMERICAN ECONOMIC REVIEW MONTH YEAR
Assuming the interest rate is close to zero and ∆debt = −∆A, then the asset accumulation
equation yields C = Y + ∆debt. Thus a second measure of the MPS is β2 = Y+ ˆ∆debtY
=
1 +ˆ∆debtY
. Analagous to equation (2), the of variance of the second measure of the MPS is
V ar(β2) = E(β2 − β)2 =1
Y 2V ar(ε∆debt) +
ˆ∆debt2
Y 4V ar(εY )(6)
It is also possible to take a weighted average over the two MPS estimates:
β3 = wβ + (1− w)β2(7)
The variance of this object is:
V ar(β3) = w2V ar(β) + (1− w)2V ar(β2) + w(1 −w)Cov(β, β2).(8)
The covariance Cov(β, β2) is not 0 because (i) the same income information is used in both
measures and (ii) the CEX income measure is correlated with the CEX spending measure.
The covariance is:
Cov(β, β2) = −ˆ∆debt
Y 3Cov(εY , εC) +
ˆ∆debtC
Y 4V ar(Y )(9)
where Cov(εY , εC) is calculated in equation (5), so (9) can be written as:
Cov(β, β2) = −ˆ∆debt
Y 3wCEXCov(εC , εYCEX
) +ˆ∆debtC
Y 4V ar(Y )(10)
Minimizing the right hand side of equation (8) with respect to w yields the value of w that
minimizes the variance of β3:
w =V ar(β2)− Cov(β, β2)/2
V ar(β) + V ar(β2)− Cov(β, β2).(11)
Appendix C: Solving the model (not for publication)
In order to reduce the number of state variables, we follow Deaton (1991) and redefine the
VOL. VOLUME NO. ISSUEONLINE APPENDIXTHE SPENDING AND DEBT RESPONSE TO MINIMUM WAGE HIKES11
problem in terms of cash-on-hand:9
Xt = (1 + r)At + Yt.(12)
Assets and cash-on-hand follow:
At+1 = Xt − Ct,(13)
Xt+1 = (1 + r)(Xt − Ct − It) + Yt+1.(14)
Thus, the borrowing constraint becomes
−
(
Xt − Yt
1 + r
)
≤ (1− π)Dt.(15)
Note that all of the variables in Xt are known at the beginning of period t. We can
thus write the individual’s problem recursively, using cash-on-hand as a state variable. In
recursive form, the household’s problem is to choose non-durables consumption and durables
investment to maximize :
Vt(Zt) = maxCt,It
{(C1−θt Dθ
t )1−γ/(1− γ) + β
∫
Vt+1(Zt+1)dF (Zt+1|Zt, Ct, It, t)}(16)
subject to the constraint in equation (15), where the state variables of the model are Zt =
(Xt,Dt, Pt), and F (.|.) gives the conditional cdf of the state variables, using equations (??),
(??), (??), and (14). Solving the model gives optimal consumption and durables investment
decision rules.
The source of uncertainty in the model is from income. We integrate over the distribution
of income by discretizing Pt using discrete state Markov Chains (Tauchen 1986).
To simulate the model, we take the initial joint distribution of the state variables from
the data. We then take draws of income from the data generating process of income. Given
9Using cash-on-hand allows us to combine assets and the transitory component of income ut into a single statevariable.
12 THE AMERICAN ECONOMIC REVIEW MONTH YEAR
the initial joint distribution of (X0,D0, P0) that we observe in the data, we use the decision
rules to obtain C0, I0, which gives us a value of (X1,D1). We take a draw for P1, which then
gives income. We repeat this for T = 200 periods. The figures presented are based on 5,000
simulations of the model.
Appendix D: Certainty and no borrowing constraints (not for publication)
Using assets instead of cash on hand as the state variable, Bellman’s equation (16) without
uncertainty is:
Vt(At,Dt, Pt) = maxCt,It
{U(Ct,Dt) + βVt+1(At+1,Dt+1, Pt+1)}.(17)
The only constraints in this case are the law of motion for assets (equation ??) and durables
(equation ??) and that final period assets must be non-negative. The first order conditions
for non-durables consumption and durables investment are, respectively:
∂Ut
∂Ct= β
∂Vt+1
∂At+1(18)
∂Vt+1
∂At+1=
∂Vt+1
∂Dt+1.(19)
Differentiating with respect to assets and the durables stock and using the envelope condition
yields, respectively:
∂Vt
∂At= β(1 + r)
∂Vt+1
∂At+1(20)
∂Vt
∂Dt=
∂Ut
∂Dt+ β
∂Vt+1
∂Dt+1(1− δ).(21)
Combining equations (19), (20), and (21) yields
β(1 + r)∂Vt+1
∂At+1=
∂Ut
∂Dt+ β
∂Vt+1
∂At+1(1− δ).(22)
VOL. VOLUME NO. ISSUEONLINE APPENDIXTHE SPENDING AND DEBT RESPONSE TO MINIMUM WAGE HIKES13
Combining equations (18) and (22) yields
(r + δ)∂Ut
∂Ct=
∂Ut
∂Dt.(23)
Inserting the specific functional forms for the utility function from equation (??) into equation
(23) yields
(r + δ)
(
1− θ
θ
)
Dt = Ct.(24)
Combining equations (18), (20), and (24) yields the Euler Equation
Ct+1 = Ct(β(1 + r))1γ .(25)
Define
PV ≡ A0 +
T∑
t=0
(
1
1 + r
)t
Yt(26)
as “full wealth”, i.e., the present value of lifetime income plus wealth. Given that the present
value of lifetime spending is equal to full wealth (and given that the annual cost of durables
is (r + δ)), the lifetime budget constraint is
T∑
t=0
(
1
1 + r
)t
(Ct + (r + δ)Dt) = PV.(27)
Inserting equation (24) into equation (27) yields
T∑
t=0
(
1
1 + r
)t(
Ct +
(
θ
1− θ
)
Ct
)
= PV.(28)
Combining equation (25) with equation (28) yields
T∑
t=0
(
1
1 + r
)t((
1 +
(
θ
1− θ
))
C0(β(1 + r))t/γ)
= PV.(29)
14 THE AMERICAN ECONOMIC REVIEW MONTH YEAR
Using the formula for an infinite sum and rearranging yields
C0 = (1− θ)
[
1− (β(1+r))1γ
1+r
1−( (β(1+r))
1γ
1+r
)T+1
]
PV(30)
where (1− θ)
[
1−(β(1+r))
1γ
1+r
1−(
(β(1+r))1γ
1+r
)T+1
]
is the marginal propensity to consume non-durables. Insert-
ing equation (24) into equation (30) yields
D0 = (θ
r + δ)
[
1− (β(1+r))1γ
1+r
1−
(
(β(1+r))1γ
1+r
)T+1
]
PV.(31)
Holding last period’s durables stock fixed, increases in this period’s durables stock can only
come from increases in investment. Thus
∂I0∂PV
∣
∣
∣
∣
D0
=∂D1
∂PV
∣
∣
∣
∣
D0
= (β(1 + r))1γ (
θ
r + δ)
[
1− (β(1+r))1γ
1+r
1−
(
(β(1+r))1γ
1+r
)T+1
]
(32)
is the marginal propensity to spend on durables. Inspection of equation (27) shows that
the marginal propensity to spend is the same for increases in assets and the present value
of lifetime income. In order to get time period 1 non-durables and durables spending, note
that equation (25) shows that consumption grows at rate (β(1+ r))1γ , and thus the marginal
propensity to consume non-durables at time 1, given an increase in full wealth at time 0, is
(β(1 + r))1γ (1− θ)
[
1−(β(1+r))
1γ
1+r
1−(
(β(1+r))1γ
1+r
)T+1
]
. To derive the time 1 durables spending response, note
that the ratio of durables to non-durables is a constant, and thus the durables stock grows
at a rate (β(1 + r))1γ . Using this result, the law of motion for durables, and equation (32)
VOL. VOLUME NO. ISSUEONLINE APPENDIXTHE SPENDING AND DEBT RESPONSE TO MINIMUM WAGE HIKES15
yields the marginal propensity to spend on durables at time 1:
∂I1∂PV
∣
∣
∣
∣
D0
=∂D2
∂PV
∣
∣
∣
∣
D0
− (1− δ)∂D1
∂PV
∣
∣
∣
∣
D0
= (β(1 + r))1γ∂D1
∂PV
∣
∣
∣
∣
D0
− (1− δ)∂D1
∂PV
∣
∣
∣
∣
D0
=[
(β(1 + r))1γ − (1− δ)
] ∂D1
∂PV
∣
∣
∣
∣
D0
=[
(β(1 + r))1γ − (1− δ)
]
(β(1 + r))1γ (
θ
r + δ)
[
1− (β(1+r))1γ
1+r
1−
(
(β(1+r))1γ
1+r
)T+1
]
.(33)
Solving for time period 2 spending propensities is straightforward.
16 THE AMERICAN ECONOMIC REVIEW MONTH YEAR
REFERENCES
Agarwal, Sumit, Chunlin Liu, and Nicholas Souleles. 2007. ”The Reaction of Consumption
and Debt to Tax Rebates: Evidence from the Consumer Credit Data.” Journal of Political
Economy 115 (6): 986-1019.
Attanasio, Orazio P., and G. Weber. 1995. ”Is Consumption Growth Consistent with In-
tertemporal Optimization? Evidence from the Consumer Expenditure Survey.” Journal of
Political Economy 103 (6): 1121-1157.
Baughman, Reagan, and Stacy Dickert-Conlin. 2007. “The Earned Income Tax Credit and
Fertility. ” Journal of Population Economics 22 (3): 537-563.
Deaton, Angus. 1991. “Saving and Liquidity Constraints.’ Econometrica 59 (5): 1221-1248.
Johnson, Kathleen. 2007. ”Recent Developments in the Credit Card Market and the Finan-
cial Obligations Ratio.” In Household Credit Usage: Personal Debt and Mortgages, edited
by Sumit Agarwal and Brent W. Ambrose, 13-36. Palgrave-McMillian Publishing.
Tauchen, George. 1986. “Finite state Markov chain approximations to univariate and vector
autoregressions. ” Economics Letters 20: 177-181.
VOL.VOLUME
NO.IS
SUEONLIN
EAPPENDIX
THE
SPENDIN
GAND
DEBT
RESPONSE
TO
MIN
IMUM
WAGEHIK
ES17
NOT FOR PUBLICATIONTable A1
Summary Statistics
Units with Si=0 Units with Si� 0.2 Income � $20,000 Income < $20,000in initial survey in initial survey at application at application
Variable Mean Std Dev Mean Std Dev Mean Std Dev Mean Std Dev
A. Consumer Expenditure Survey, 1983-2008Real average quarterly spending 10,938 7,792 6,462 4,731 Real Durables 1,818 4,932 890 3,087 Real Nondurables and services 9,120 5,243 5,573 3,059
Real before tax family nonasset annual income, first and last surveys 61,896 43,882 21,074 16,148Share of income from MW earners 0.00 0.00 0.68 0.30
Member 1 age 40.4 11.1 35.6 12.8Number of adults 1.90 0.81 1.79 0.85Number of kids under 18 0.82 1.12 0.88 1.22
Number of unit-surveys 178,075 15,834Number of units 53,629 5,206
B. Survey of Income and Program Participation, 1986-2007Real before tax family nonproperty 52,341 35,554 25,914 20,210 annual income in initial surveyShare of income from MW earners 0.00 0.00 0.62 0.31
Head age 41.48 10.97 38.21 12.10Number of adults 1.93 0.77 1.78 0.74Number of kids under 18 0.88 1.12 1.02 1.22
Number of household-surveys 420,634 39,472Number of households 52775 5,176
18
THE
AMERIC
AN
ECONOMIC
REVIE
WMONTH
YEAR
NOT FOR PUBLICATIONTable A1
Summary Statistics
Units with Si=0 Units with Si� 0.2 Income � $20,000 Income < $20,000in initial survey in initial survey at application at application
Variable Mean Std Dev Mean Std Dev Mean Std Dev Mean Std Dev
C. Current Population Survey, 1980-2007Real annualized family income 38,333 22,471 21,433 15,088Share of income from MW earners 0 0 0.65 0.32
Head age 42.1 10.9 41.2 12.2Number of adults 2.15 0.86 2.22 0.92Number of kids under 18 0.89 1.11 0.93 1.18
Number of household-surveys 688,356 93,846
D. Credit Card and Credit Bureau, 1995-2008Annual salary income at application 74,623 49,576 14,033 9,381Fico Score 737 84 700 73Active Credit Cards 3.0 2.6 2.3 2.6Credit Card Balance on All Cards 6,162 7,775 4,713 4,368Home Equity Balance 703 5,376 753 8,653Mortgage Balance 20,807 163,738 30,595 118,130Auto Balance 3,314 8,365 3,432 7,117
Number of observations 4,028,327 582,170Number of consumers 317,116 31,624
Notes: Real spending and income in 2005 dollars. All CEX, SIPP, and CPS descriptive statistics are weighted.
VOL. VOLUME NO. ISSUEONLINE APPENDIXTHE SPENDING AND DEBT RESPONSE TO MINIMUM WAGE HIKES19
NOT FOR PUBLICATIONTable A2
Minimum Wage Changes, 1982-2008
Date New Change Date New ChangeU.S. Apr-90 3.80 0.45U.S. Apr-91 4.25 0.45U.S. Oct-96 4.75 0.50U.S. Sep-97 5.15 0.40U.S. Jul-07 5.85 0.70U.S. Jul-08 6.55 0.70
Alaska Jan-03 7.15 1.50 Illinois Jan-04 5.50 0.35Arizona Jan-07 6.75 1.60 Illinois Jan-05 6.50 1.00Arizona Jan-08 6.90 0.15 Illinois Jul-07 7.50 1.00Arkansas Oct-06 6.25 1.10 Illinois Jul-08 7.75 0.25California Jul-88 4.25 0.90 Iowa Jan-90 3.85 0.50California Mar-97 5.00 0.25 Iowa Jan-91 4.25 0.40California Sep-97 5.15 0.15 Iowa Jan-92 4.65 0.40California Mar-98 5.75 0.60 Iowa Oct-96 4.75 0.10California Jan-01 6.25 0.50 Iowa Apr-07 6.20 1.05California Jan-02 6.75 0.50 Iowa Jan-08 7.25 1.05California Jan-07 7.50 0.75 Kentucky Jun-07 5.85 0.70California Jan-08 8.00 0.50 Maine Jan-85 3.45 0.10Colorado Jan-07 6.85 1.70 Maine Jan-86 3.55 0.10Colorado Jan-08 7.02 0.17 Maine Jan-87 3.65 0.10Connecticut Oct-87 3.75 0.38 Maine Jan-88 3.75 0.10Connecticut Oct-88 4.25 0.50 Maine Jan-90 3.85 0.10Connecticut Apr-91 4.27 0.02 Maine Apr-91 4.25 0.40Connecticut Oct-96 4.77 0.50 Maine Jan-02 5.75 0.60Connecticut Oct-96 4.77 0.50 Maine Jan-02 5.75 0.60Connecticut Mar-97 5.00 0.23 Maine Jan-03 6.25 0.50Connecticut Sep-97 5.18 0.18 Maine Jan-05 6.35 0.10Connecticut Jan-99 5.65 0.47 Maine Jan-06 6.50 0.15Connecticut Jan-00 6.15 0.50 Maine Oct-06 6.75 0.25Connecticut Jan-01 6.40 0.25 Maine Oct-07 7.00 0.25Connecticut Jan-02 6.70 0.30 Maine Oct-08 7.25 0.25Connecticut Jan-03 6.90 0.20 Maryland Jan-07 6.15 1.00Connecticut Jan-04 7.10 0.20 Massachusetts Jul-86 3.55 0.20Connecticut Jan-06 7.40 0.30 Massachusetts Jul-87 3.65 0.10Connecticut Jan-07 7.65 0.25 Massachusetts Jul-88 3.75 0.10Delaware May-99 5.65 0.50 Massachusetts Apr-90 3.80 0.05Delaware Oct-00 6.15 0.50 Massachusetts Jan-96 4.75 0.50Delaware Jan-07 6.65 0.50 Massachusetts Jan-97 5.25 0.50Delaware Jan-08 7.15 0.50 Massachusetts Jan-00 6.00 0.75Florida Jan-06 6.40 1.25 Massachusetts Jan-01 6.75 0.75Florida Jan-07 6.67 0.27 Massachusetts Jan-07 7.50 0.75Florida Jan-08 6.79 0.12 Massachusetts Jan-08 8.00 0.50Hawaii Jan-88 3.85 0.50 Michigan Oct-06 6.95 1.80Hawaii Mar-91 4.25 0.40 Michigan Jul-07 7.15 0.20Hawaii Apr-92 4.75 0.50 Michigan Jul-08 7.40 0.25Hawaii Jan-93 5.25 0.50 Minnesota Jan-88 3.55 0.20Hawaii Jan-02 5.75 0.50 Minnesota Jan-89 3.85 0.30Hawaii Jan-03 6.25 0.50 Minnesota Jan-90 3.95 0.10Hawaii Jan-06 6.75 0.50 Minnesota Jan-91 4.25 0.30Hawaii Jan-07 7.25 0.50 Minnesota Aug-05 6.15 1.00
20 THE AMERICAN ECONOMIC REVIEW MONTH YEAR
NOT FOR PUBLICATIONTable A2 -cont-
Minimum Wage Changes, 1982-2008
Date New Change Date New Change
Missouri Jan-07 6.50 1.35 Rhode Island Jul-86 3.55 0.20Missouri Jan-08 6.65 0.15 Rhode Island Jul-87 3.65 0.10Montana Jan-07 6.15 1.00 Rhode Island Jul-88 4.00 0.35Nevada Nov-06 6.15 1.00 Rhode Island Aug-89 4.25 0.25Nevada Jan-07 6.33 0.18 Rhode Island Apr-91 4.45 0.20New Hampshire Jan-87 3.45 0.10 Rhode Island Oct-96 4.75 0.30New Hampshire Jan-88 3.55 0.10 Rhode Island Jul-99 5.65 0.50New Hampshire Jan-89 3.65 0.10 Rhode Island Sep-00 6.15 0.50New Hampshire Jan-90 3.75 0.10 Rhode Island Jan-04 6.75 0.60New Hampshire Apr-90 3.80 0.05 Rhode Island Mar-06 7.10 0.35New Hampshire Jan-91 3.85 0.05 Rhode Island Jan-07 7.40 0.30New Hampshire Apr-91 4.25 0.40 South Dakota Jul-07 5.85 0.70New Hampshire Sep-07 6.50 1.35 Vermont Jul-86 3.45 0.10New Hampshire Sep-08 7.25 0.75 Vermont Jul-87 3.55 0.10New Jersey Apr-92 5.05 0.80 Vermont Jan-89 3.65 0.10New Jersey Sep-97 5.15 0.10 Vermont Jul-89 3.75 0.10New Jersey Oct-05 6.15 1.00 Vermont Apr-90 3.85 0.10New Jersey Oct-06 7.15 1.00 Vermont Apr-91 4.25 0.40New Mexico Jan-08 6.50 0.65 Vermont Jan-95 4.50 0.25New York Jan-05 6.00 0.85 Vermont Jan-96 4.75 0.25New York Jan-06 6.75 0.75 Vermont Jul-97 5.15 0.40New York Jan-07 7.15 0.40 Vermont Sep-97 5.25 0.10North Carolina Jan-07 6.15 1.00 Vermont Nov-99 5.75 0.50North Dakota Jul-07 5.85 0.70 Vermont Jan-01 6.25 0.50North Dakota Jul-07 5.85 0.70 Vermont Jan-01 6.25 0.50Ohio Jan-07 6.85 1.70 Vermont Jan-04 6.75 0.50Ohio Jan-08 7.00 0.15 Vermont Jan-05 7.00 0.25Oregon Sep-89 3.85 0.50 Vermont Jan-06 7.25 0.25Oregon Jan-90 4.25 0.40 Vermont Jan-07 7.53 0.28Oregon Jan-91 4.75 0.50 Vermont Jan-08 7.68 0.15Oregon Jan-97 5.50 0.75 Washington Jan-89 3.85 0.50Oregon Jan-98 6.00 0.50 Washington Jan-90 4.25 0.40Oregon Jan-99 6.50 0.50 Washington Jan-94 4.90 0.65Oregon Jan-03 6.90 0.40 Washington Sep-97 5.15 0.25Oregon Jan-04 7.05 0.15 Washington Jan-99 5.70 0.55Oregon Jan-05 7.25 0.20 Washington Jan-00 6.50 0.80Oregon Jan-06 7.50 0.25 Washington Jan-01 6.72 0.22Oregon Jan-07 7.80 0.30 Washington Jan-02 6.90 0.18Oregon Jan-08 7.95 0.15 Washington Jan-03 7.01 0.11Pennslyvania Feb-89 3.70 0.35 Washington Jan-04 7.16 0.15Pennslyvania Apr-90 3.80 0.10 Washington Jan-05 7.35 0.19Pennslyvania Jan-07 6.25 1.10 Washington Jan-06 7.63 0.28Pennslyvania Jul-07 7.15 0.90 Washington Jan-07 7.93 0.30
Washington Jan-08 8.07 0.14West Virginia Jul-06 5.85 0.70West Virginia Jul-07 6.55 0.70West Virginia Jul-08 7.25 0.70Wisconsin Jun-05 5.70 0.55Wisconsin Jun-06 6.50 0.80
VOL.VOLUME
NO.IS
SUEONLIN
EAPPENDIX
THE
SPENDIN
GAND
DEBT
RESPONSE
TO
MIN
IMUM
WAGEHIK
ES21
NOT FOR PUBLICATIONTable A3
Employment, Hours, and Wage Responses to a Minimum Wage IncreaseCurrent Population Survey, 1980-2007
Sample: Hourly Wage Workers
Share of income Employment Hours Hourly Wage
from minimum Total Head Spouse Total Head Spouse All Head Spouse
wage jobs (Si)
� -0.005 -0.001 -0.004 -0.15 -0.03 0.03 -0.03 0.01 0.09
(0.002) (0.002) (0.002) (0.11) (0.06) (0.07) (0.08) (0.06) (0.06)
688,356 672,523 543,129 688,356 619,073 438,720 688,356 513,895 378,890
� � 0.009 -0.003 0.015 1.12 0.31 0.71 0.47 0.41 0.42
(0.009) (0.006) (0.008) (0.40) (0.21) (0.27) (0.19) (0.15) (0.11)
121,275 117,203 102,764 121,275 104,554 91,038 121,275 82,311 86,859
� � � � 0.011 -0.001 0.017 0.82 -0.02 0.67 0.54 0.40 0.40
(0.011) (0.008) (0.010) (0.49) (0.26) (0.33) (0.20) (0.15) (0.13)93,846 90,453 75,929 93,846 79,487 66,258 93,846 63,230 63,164
22
THE
AMERIC
AN
ECONOMIC
REVIE
WMONTH
YEAR
NOT FOR PUBLICATIONTable A4
Summary Statistics, 1989, 1992, 1995, 1998, 2001, 2004, and 2007 Survey of Consumer Finances
Variable Households with Si=0 Households with Si� 0.2
Mean Median Mean MedianFamily income 54,106 40,735 20,008 14,295Value of durables (Dit) 19,579 12,590 9,232 5,146Value of loans against durables 6,447 0 3,911 0Financial assets 136,384 17,035 24,549 824Net financial assets (Ait) 129,937 11,367 20,637 180
Voluntary equity (Ait+(1- )Dit) 141,684 20,889 26,176 2,842
Homeowner (=1 if yes) 0.62 1.00 0.40 0.00Age of head 41.7 41.0 37.1 35.0
Number of households 79,385 3,842
Notes: Real income, assets, and debt in 2005 dollars. All descriptive statistics are weighted. Income variable is pre-taxearnings of husband and wife. Financial assets includes stocks, bonds, checking and money market accounts, lessliabilities against these. Net financial assets is financial assets less value of loans against durables.